
import numpy as np
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt

sc.settings.verbosity = 3             # verbosity: errors (0), warnings (1), info (2), hints (3)
#sc.logging.print_versions()

sc.settings.set_figure_params(dpi=80)
%matplotlib inline


# Load the stored data object
save_file = './data/results/scanpy_dr_covid.h5ad'
adata = sc.read_h5ad(save_file)


print(adata.X.shape)

(5646, 3090)


adata2 = adata.raw.to_adata() 

adata2.uns['log1p']['base']=None

# check that the matrix looks like noramlized counts
print(adata2.X[1:10,1:10])

  (0, 5)	0.9678403
  (1, 5)	0.5124039


var_genes_all = adata.var.highly_variable

print("Highly variable genes: %d"%sum(var_genes_all))

Highly variable genes: 3090


sc.pp.highly_variable_genes(adata2, min_mean=0.0125, max_mean=3, min_disp=0.5, batch_key = 'sample')

print("Highly variable genes intersection: %d"%sum(adata2.var.highly_variable_intersection))

print("Number of batches where gene is variable:")
print(adata2.var.highly_variable_nbatches.value_counts())

var_genes_batch = adata2.var.highly_variable_nbatches > 0

extracting highly variable genes

/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  hvg = hvg.append(missing_hvg, ignore_index=True)
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  hvg = hvg.append(missing_hvg, ignore_index=True)
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  hvg = hvg.append(missing_hvg, ignore_index=True)
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  hvg = hvg.append(missing_hvg, ignore_index=True)
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  hvg = hvg.append(missing_hvg, ignore_index=True)

    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
Highly variable genes intersection: 147
Number of batches where gene is variable:
0    8103
1    4754
2    3154
3    1638
4     705
5     251
6     147
Name: highly_variable_nbatches, dtype: int64

/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  hvg = hvg.append(missing_hvg, ignore_index=True)


print("Any batch var genes: %d"%sum(var_genes_batch))
print("All data var genes: %d"%sum(var_genes_all))
print("Overlap: %d"%sum(var_genes_batch & var_genes_all))
print("Variable genes in all batches: %d"%sum(adata2.var.highly_variable_nbatches == 6))
print("Overlap batch instersection and all: %d"%sum(var_genes_all & adata2.var.highly_variable_intersection))

Any batch var genes: 10649
All data var genes: 3090
Overlap: 3086
Variable genes in all batches: 147
Overlap batch instersection and all: 147


var_select = adata2.var.highly_variable_nbatches > 2
var_genes = var_select.index[var_select]
len(var_genes)

2741


sc.external.pp.bbknn(adata2, batch_key='sample', n_pcs=30)  # running bbknn 1.3.6

# then run umap on the integrated space
sc.tl.umap(adata2)
sc.tl.tsne(adata2)

computing batch balanced neighbors
	finished: added to `.uns['neighbors']`
	`.obsp['distances']`, distances for each pair of neighbors
	`.obsp['connectivities']`, weighted adjacency matrix (0:00:02)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:09)
computing tSNE
    using 'X_pca' with n_pcs = 50
    using sklearn.manifold.TSNE
    finished: added
    'X_tsne', tSNE coordinates (adata.obsm) (0:00:13)


fig, axs = plt.subplots(2, 2, figsize=(10,8),constrained_layout=True)
sc.pl.tsne(adata2, color="sample", title="BBKNN Corrected tsne", ax=axs[0,0], show=False)
sc.pl.tsne(adata, color="sample", title="Uncorrected tsne", ax=axs[0,1], show=False)
sc.pl.umap(adata2, color="sample", title="BBKNN Corrected umap", ax=axs[1,0], show=False)
sc.pl.umap(adata, color="sample", title="Uncorrected umap", ax=axs[1,1], show=False)

/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

<AxesSubplot: title={'center': 'Uncorrected umap'}, xlabel='UMAP1', ylabel='UMAP2'>


save_file = './data/results/scanpy_bbknn_corrected_covid.h5ad'
adata2.write_h5ad(save_file)


# create a new object with lognormalized counts
adata_combat = sc.AnnData(X=adata.raw.X, var=adata.raw.var, obs = adata.obs)


# first store the raw data 
adata_combat.raw = adata_combat

# run combat
sc.pp.combat(adata_combat, key='sample')

Standardizing Data across genes.

Found 6 batches

Found 0 numerical variables:
	

Found 25 genes with zero variance.
Fitting L/S model and finding priors

Finding parametric adjustments

/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/preprocessing/_combat.py:340: RuntimeWarning: invalid value encountered in divide
  (abs(g_new - g_old) / g_old).max(), (abs(d_new - d_old) / d_old).max()
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/preprocessing/_combat.py:340: RuntimeWarning: divide by zero encountered in divide
  (abs(g_new - g_old) / g_old).max(), (abs(d_new - d_old) / d_old).max()

Adjusting data


sc.pp.highly_variable_genes(adata_combat)
print("Highly variable genes: %d"%sum(adata_combat.var.highly_variable))
sc.pl.highly_variable_genes(adata_combat)


sc.pp.pca(adata_combat, n_comps=30, use_highly_variable=True, svd_solver='arpack')

sc.pp.neighbors(adata_combat)

sc.tl.umap(adata_combat)
sc.tl.tsne(adata_combat)

extracting highly variable genes
    finished (0:00:01)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
Highly variable genes: 3915

computing PCA
    on highly variable genes
    with n_comps=30
    finished (0:00:00)
computing neighbors
    using 'X_pca' with n_pcs = 30
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:07)
computing tSNE
    using 'X_pca' with n_pcs = 30
    using sklearn.manifold.TSNE
    finished: added
    'X_tsne', tSNE coordinates (adata.obsm) (0:00:12)


# compare var_genes
var_genes_combat = adata_combat.var.highly_variable
print("With all data %d"%sum(var_genes_all))
print("With combat %d"%sum(var_genes_combat))
print("Overlap %d"%sum(var_genes_all & var_genes_combat))

print("With 2 batches %d"%sum(var_select))
print("Overlap %d"%sum(var_genes_combat & var_select))

With all data 3090
With combat 3915
Overlap 2268
With 2 batches 2741
Overlap 2040


fig, axs = plt.subplots(2, 2, figsize=(10,8),constrained_layout=True)
sc.pl.tsne(adata2, color="sample", title="BBKNN tsne", ax=axs[0,0], show=False)
sc.pl.tsne(adata_combat, color="sample", title="Combat tsne", ax=axs[0,1], show=False)
sc.pl.umap(adata2, color="sample", title="BBKNN umap", ax=axs[1,0], show=False)
sc.pl.umap(adata_combat, color="sample", title="Combat umap", ax=axs[1,1], show=False)

/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

<AxesSubplot: title={'center': 'Combat umap'}, xlabel='UMAP1', ylabel='UMAP2'>


#save to file
save_file = './data/results/scanpy_combat_corrected_covid.h5ad'
adata_combat.write_h5ad(save_file)


# split per batch into new objects.
batches = adata.obs['sample'].cat.categories.tolist()
alldata = {}
for batch in batches:
    alldata[batch] = adata2[adata2.obs['sample'] == batch,]

alldata

{'covid_1': View of AnnData object with n_obs × n_vars = 844 × 18752
     obs: 'type', 'sample', 'batch', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'percent_mt2', 'n_counts', 'n_genes', 'percent_chrY', 'XIST-counts', 'S_score', 'G2M_score', 'phase', 'doublet_scores', 'predicted_doublets', 'doublet_info'
     var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection'
     uns: 'doublet_info_colors', 'hvg', 'log1p', 'neighbors', 'pca', 'sample_colors', 'tsne', 'umap'
     obsm: 'X_pca', 'X_tsne', 'X_umap'
     obsp: 'connectivities', 'distances',
 'covid_15': View of AnnData object with n_obs × n_vars = 583 × 18752
     obs: 'type', 'sample', 'batch', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'percent_mt2', 'n_counts', 'n_genes', 'percent_chrY', 'XIST-counts', 'S_score', 'G2M_score', 'phase', 'doublet_scores', 'predicted_doublets', 'doublet_info'
     var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection'
     uns: 'doublet_info_colors', 'hvg', 'log1p', 'neighbors', 'pca', 'sample_colors', 'tsne', 'umap'
     obsm: 'X_pca', 'X_tsne', 'X_umap'
     obsp: 'connectivities', 'distances',
 'covid_17': View of AnnData object with n_obs × n_vars = 1018 × 18752
     obs: 'type', 'sample', 'batch', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'percent_mt2', 'n_counts', 'n_genes', 'percent_chrY', 'XIST-counts', 'S_score', 'G2M_score', 'phase', 'doublet_scores', 'predicted_doublets', 'doublet_info'
     var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection'
     uns: 'doublet_info_colors', 'hvg', 'log1p', 'neighbors', 'pca', 'sample_colors', 'tsne', 'umap'
     obsm: 'X_pca', 'X_tsne', 'X_umap'
     obsp: 'connectivities', 'distances',
 'ctrl_5': View of AnnData object with n_obs × n_vars = 1028 × 18752
     obs: 'type', 'sample', 'batch', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'percent_mt2', 'n_counts', 'n_genes', 'percent_chrY', 'XIST-counts', 'S_score', 'G2M_score', 'phase', 'doublet_scores', 'predicted_doublets', 'doublet_info'
     var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection'
     uns: 'doublet_info_colors', 'hvg', 'log1p', 'neighbors', 'pca', 'sample_colors', 'tsne', 'umap'
     obsm: 'X_pca', 'X_tsne', 'X_umap'
     obsp: 'connectivities', 'distances',
 'ctrl_13': View of AnnData object with n_obs × n_vars = 1132 × 18752
     obs: 'type', 'sample', 'batch', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'percent_mt2', 'n_counts', 'n_genes', 'percent_chrY', 'XIST-counts', 'S_score', 'G2M_score', 'phase', 'doublet_scores', 'predicted_doublets', 'doublet_info'
     var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection'
     uns: 'doublet_info_colors', 'hvg', 'log1p', 'neighbors', 'pca', 'sample_colors', 'tsne', 'umap'
     obsm: 'X_pca', 'X_tsne', 'X_umap'
     obsp: 'connectivities', 'distances',
 'ctrl_14': View of AnnData object with n_obs × n_vars = 1041 × 18752
     obs: 'type', 'sample', 'batch', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'percent_mt2', 'n_counts', 'n_genes', 'percent_chrY', 'XIST-counts', 'S_score', 'G2M_score', 'phase', 'doublet_scores', 'predicted_doublets', 'doublet_info'
     var: 'gene_ids', 'feature_types', 'genome', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection'
     uns: 'doublet_info_colors', 'hvg', 'log1p', 'neighbors', 'pca', 'sample_colors', 'tsne', 'umap'
     obsm: 'X_pca', 'X_tsne', 'X_umap'
     obsp: 'connectivities', 'distances'}


import scanorama


#subset the individual dataset to the variable genes we defined at the beginning
alldata2 = dict()
for ds in alldata.keys():
    print(ds)
    alldata2[ds] = alldata[ds][:,var_genes]

#convert to list of AnnData objects
adatas = list(alldata2.values())

# run scanorama.integrate
scanorama.integrate_scanpy(adatas, dimred = 50)

covid_1
covid_15
covid_17
ctrl_5
ctrl_13
ctrl_14
Found 2741 genes among all datasets
[[0.         0.78216123 0.3497053  0.57345972 0.45260664 0.40521327]
 [0.         0.         0.55403087 0.5        0.31903945 0.37049743]
 [0.         0.         0.         0.36770428 0.10314342 0.1453831 ]
 [0.         0.         0.         0.         0.86770428 0.72568093]
 [0.         0.         0.         0.         0.         0.85424028]
 [0.         0.         0.         0.         0.         0.        ]]
Processing datasets (3, 4)
Processing datasets (4, 5)
Processing datasets (0, 1)
Processing datasets (3, 5)
Processing datasets (0, 3)
Processing datasets (1, 2)
Processing datasets (1, 3)
Processing datasets (0, 4)
Processing datasets (0, 5)
Processing datasets (1, 5)
Processing datasets (2, 3)
Processing datasets (0, 2)
Processing datasets (1, 4)
Processing datasets (2, 5)
Processing datasets (2, 4)


#scanorama adds the corrected matrix to adata.obsm in each of the datasets in adatas.

adatas[0].obsm['X_scanorama'].shape

(844, 50)


# Get all the integrated matrices.
scanorama_int = [ad.obsm['X_scanorama'] for ad in adatas]

# make into one matrix.
all_s = np.concatenate(scanorama_int)
print(all_s.shape)

# add to the AnnData object, create a new object first
adata_sc = adata.copy()
adata_sc.obsm["Scanorama"] = all_s

(5646, 50)


# tsne and umap
sc.pp.neighbors(adata_sc, n_pcs =30, use_rep = "Scanorama")
sc.tl.umap(adata_sc)
sc.tl.tsne(adata_sc, n_pcs = 30, use_rep = "Scanorama")

computing neighbors
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)
computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:08)
computing tSNE
    using sklearn.manifold.TSNE
    finished: added
    'X_tsne', tSNE coordinates (adata.obsm) (0:00:12)


fig, axs = plt.subplots(2, 2, figsize=(10,8),constrained_layout=True)
sc.pl.umap(adata2, color="sample", title="BBKNN tsne", ax=axs[0,0], show=False)
sc.pl.umap(adata, color="sample", title="Scanorama tsne", ax=axs[0,1], show=False)
sc.pl.umap(adata2, color="sample", title="BBKNN umap", ax=axs[1,0], show=False)
sc.pl.umap(adata, color="sample", title="Scanorama umap", ax=axs[1,1], show=False)

/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

<AxesSubplot: title={'center': 'Scanorama umap'}, xlabel='UMAP1', ylabel='UMAP2'>


#save to file
save_file = './data/results/scanpy_scanorama_corrected_covid.h5ad'
adata_sc.write_h5ad(save_file)


fig, axs = plt.subplots(2, 2, figsize=(10,8),constrained_layout=True)
sc.pl.umap(adata, color="sample", title="Uncorrected", ax=axs[0,0], show=False)
sc.pl.umap(adata2, color="sample", title="BBKNN", ax=axs[0,1], show=False)
sc.pl.umap(adata_combat, color="sample", title="Combat", ax=axs[1,0], show=False)
sc.pl.umap(adata_sc, color="sample", title="Scanorama", ax=axs[1,1], show=False)

/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(
/Users/asabjor/miniconda3/envs/scRNAseq2023_python/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:392: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored
  cax = scatter(

<AxesSubplot: title={'center': 'Scanorama'}, xlabel='UMAP1', ylabel='UMAP2'>

Markdown	Language	Library	Ref
CCA	R	Seurat	Cell
MNN	R/Python	Scater/Scanpy	Nat. Biotech.
Conos	R	conos	Nat. Methods
Scanorama	Python	scanorama	Nat. Biotech.

Table of Contents

Scanpy: Data integration¶

Detect variable genes¶

Data integration¶

BBKNN¶

Combat¶

Scanorama¶

Compare all¶

Your turn¶